CART Class Prediction: One Data Set

Before you begin

A gene expression data set consists of two files:

• GCT or RES file that contains gene expression data.
  Example file: all_aml_test.gct.
• CLS file that identifies the class of each sample in the gene expression data.
  Example file: all_aml_test.cls.

learn more: file formats

Step 1: PreprocessDataset

Preprocess gene expression data to remove platform noise and genes that have little variation. Note: If preprocessing the data removes relevant biological information, skip this step.

Considerations

• PreprocessDataset can preprocess the data in one or more ways (in this order):
  1. Set threshold and ceiling values. Any value lower/higer than the threshold/ceiling value is reset to the threshold/ceiling value.
  2. Convert each expression value to the log base 2 of the value.
  3. Remove genes (rows) if a given number of its sample values are less than a given threshold.
  4. Remove genes (rows) that do not have a minimum fold change or expression variation.
  5. Discretize or normalize the data.
• When using ratios to compare gene expression between samples, convert values to log base 2 of the value to bring up- and down-regulated genes to the same scale. For example, ratios of 2 and .5 indicating two-fold changes for up- and down-regulated expression, respectively, are converted to +1 and -1.
• If you did not generate the expression data, check whether preprocessing steps have already been taken before running the PreprocessDataset module.

learn more: PreprocessDataset

Step 2: CARTXValidation

CARTXValidation runs the CART class prediction method iteratively against the known data set. For each iteration, it leaves one sample out, builds the classifier using the remaining samples, and then tests the classifier on the sample left out. It creates a prediction results file that assesses the accuracy of the classifiers.

learn more: CARTXValidation

Step 3: PredictionResultsViewer

Display the prediction results file (*.pred.odf). The viewer lists each sample, its actual class, its predicted class, and prediction error rates. Prediction error rates are averaged across all iterations.

Considerations

• The PredictionResultsViewer provides an absolute error rate (incorrect cases/total cases) and an ROC error rate (fraction of true positives versus the fraction of false positives). Use the ROC error rate for comparing results across data sets.

learn more: PredictionResultsViewer

Building a classifier (model) file

CARTXValidation does not save the classifiers that it generates. Typically, an analyst builds a classifier using one data set and tests the classifier using a second data set. It is rare to build a classifier (model) file using one data set without having a second data set available for testing; however, it is possible. To build a classifier (model) file using one data set, run the CART module: specify the one data set as the training data set.

learn more: CART